Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding

Accurate estimation of regional and global patterns of ecosystem respiration (ER) is crucial to improve the understanding of terrestrial carbon cycles and the predictive ability of the global carbon budget. However, large uncertainties still exist in regional and global ER estimation due to the drawbacks of modeling methods. Based on eddy covariance ER data from 132 sites in China from 2002 to 2020, we established Intelligent Random Forest (IRF) models that integrated ecological understanding with machine learning techniques to estimate ER. The results showed that the IRF models performed better than semiempirical models and machine learning algorithms. The observed data revealed that gross primary productivity (GPP), living plant biomass, and soil organic carbon (SOC) were of great importance in controlling the spatiotemporal variability of ER across China. An optimal model governed by annual GPP, living plant biomass, SOC, and air temperature (IRF‐04 model) matched 93% of the spatiotemporal variation in site‐level ER, and was adopted to evaluate the spatiotemporal pattern of ER in China. Using the optimal model, we obtained that the annual value of ER in China ranged from 5.05 to 5.84 Pg C yr−1 between 2000 and 2020, with an average value of 5.53 ± 0.22 Pg C yr−1. In this study, we suggest that future models should integrate process‐based and data‐driven approaches for understanding and evaluating regional and global carbon budgets. Plain Language Summary With China already committing to achieve carbon neutrality before 2060, an accurate assessment of land carbon sink and its flux rate in China is an increasingly important area in global change ecology. In this essay, a high‐efficiency and accurate simulation method was introduced in this field; This method is particularly useful in the assessment of carbon sink and its flux rate in China by combining with reliable observation flux data. Using this new method, a reliable and reasonable value of carbon flux (ecosystem respiration) was obtained. Meanwhile, that method provides a better understanding of the mechanism governing the spatiotemporal variability of carbon flux. Therefore, this present study has gone some way toward enhancing our understanding of a comprehensive assessment and analysis of land carbon sink in China. Key Points A model integrating ecological knowledge and machine learning was established to estimate ecosystem respiration (ER) in China The spatiotemporal patterns of ER are significa